Musical information retrieval. Signal Analysis and Feature Extraction using Python

Musical information retrieval. Signal Analysis and Feature Extraction using Python
Author :
Publisher : GRIN Verlag
Total Pages : 40
Release :
ISBN-10 : 9783346455246
ISBN-13 : 3346455246
Rating : 4/5 (246 Downloads)

Book Synopsis Musical information retrieval. Signal Analysis and Feature Extraction using Python by : M. Sai Chaitanya

Download or read book Musical information retrieval. Signal Analysis and Feature Extraction using Python written by M. Sai Chaitanya and published by GRIN Verlag. This book was released on 2021-08-02 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research Paper (postgraduate) from the year 2021 in the subject Musicology - Miscellaneous, grade: 8.0, , course: IMSc Mathematics and Computing, language: English, abstract: This work gives a comprehensive overview of research on the multidisciplinary field of Music Information Retrieval (MIR). MIR uses knowledge from areas as diverse as signal processing, machine learning, information and music theory. The Main Feature of this work is to explore how this knowledge can be used for the development of novel methodologies for browsing and retrieval on large music collections, a hot topic given recent advances in online music distribution and searching. Emphasis would be given to audio signal processing techniques. Music information retrieval (MIR) is the interdisciplinary science of retrieving information from music. MIR is a small but growing field of research with many realworld applications. Those involved in MIR may have a background in musicology, sychoacoustics, psychology, academic music study, signal processing, informatics, machine learning, optical music recognition, computational intelligence or some combination of these. MIR is being used by businesses and academics to categorize, manipulate and even create music. One of the classical MIR research topics is genre classification, which is categorizing music items into one of pre-defined genres such as classical, jazz, rock, etc. Mood classification, artist classification, and music tagging are also popular topics.


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